Multinomial_Logistic_Regression.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### **Logistic Regression**"
      ],
      "metadata": {
        "id": "Tm6t10EojRCq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "6jdUNEYODFG6"
      },
      "outputs": [],
      "source": [
        "import sklearn\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris=load_iris()\n",
        "X=iris['data']\n",
        "y=iris['target']"
      ],
      "metadata": {
        "id": "KJy9PjMfDToY"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6v7ma2myFky9",
        "outputId": "045a5bfa-e4dd-44b0-ce1e-db80e9533a12"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150,)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Preprocess the Data\n",
        "scaler=StandardScaler()\n",
        "X=scaler.fit_transform(X)\n",
        "X=torch.tensor(X,dtype=torch.float32)\n",
        "y=torch.tensor(y,dtype=torch.float32)\n"
      ],
      "metadata": {
        "id": "E4SqtB9gEdnv"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.2,random_state=32)"
      ],
      "metadata": {
        "id": "iiIwowFfFJho"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hGtcvnFNF64Q",
        "outputId": "addf03e1-048b-47a2-aaca-8438609ab2c5"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([120, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class LogisticRegression(nn.Module):\n",
        "  def __init__(self,n_features,h1):\n",
        "    super(LogisticRegression,self).__init__()\n",
        "    self.layer1=nn.Linear(n_features,h1)\n",
        "    self.layer2=nn.Linear(h1,3)\n",
        "    self.softmax=nn.Softmax(dim=1)\n",
        "\n",
        "  def forward(self,x):\n",
        "    out=F.relu(self.layer1(x))\n",
        "    out=F.relu(self.layer2(out))\n",
        "    out=self.softmax(out)\n",
        "    return out"
      ],
      "metadata": {
        "id": "6LrJNWlXF_ff"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def train(model,X,y,epocs=50,optimizer=None,loss_fn=None,bsz=20,seed=20):\n",
        "  torch.manual_seed(seed)\n",
        "  cost=[]\n",
        "  for epoch in range(epocs):\n",
        "    suffle_idx=torch.randperm(y.size(0))\n",
        "    batches =torch.split(suffle_idx,bsz)\n",
        "    for batch_idx in batches:\n",
        "      ###1. Compute the output\n",
        "      yhat=model(X[batch_idx])\n",
        "\n",
        "      ###2. Compute the error\n",
        "    \n",
        "      loss=loss_fn(yhat,y[batch_idx].long())\n",
        "\n",
        "      ###3. Compute the Gradient\n",
        "      optimizer.zero_grad()\n",
        "      loss.backward()\n",
        "\n",
        "      ###4. Update the Parameters\n",
        "      optimizer.step()\n",
        "\n",
        "      ### Log the loss\n",
        "    with torch.no_grad():\n",
        "      yhat=model(X)\n",
        "      curr_loss=loss_fn(yhat,y.long())\n",
        "      print('Epoc:%3d ' % epoch,end=\"\")\n",
        "      print('BCE %.2f' % curr_loss)\n",
        "      cost.append(curr_loss)\n",
        "  return cost\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "WlXCB9VovaeN"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=LogisticRegression(n_features=X_train.size(1),h1=20)\n",
        "optimizer=torch.optim.Adam(model.parameters(),lr=.001)\n",
        "loss_fn=nn.CrossEntropyLoss()"
      ],
      "metadata": {
        "id": "JHsTBH3-HgnT"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cost =train(model,X_train,y_train,optimizer=optimizer,loss_fn=loss_fn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EifyzkCYIEhr",
        "outputId": "cbb8d116-7812-49cd-eef3-d17b657a161a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoc:  0 BCE 1.10\n",
            "Epoc:  1 BCE 1.10\n",
            "Epoc:  2 BCE 1.09\n",
            "Epoc:  3 BCE 1.08\n",
            "Epoc:  4 BCE 1.08\n",
            "Epoc:  5 BCE 1.07\n",
            "Epoc:  6 BCE 1.06\n",
            "Epoc:  7 BCE 1.05\n",
            "Epoc:  8 BCE 1.04\n",
            "Epoc:  9 BCE 1.03\n",
            "Epoc: 10 BCE 1.02\n",
            "Epoc: 11 BCE 1.01\n",
            "Epoc: 12 BCE 1.00\n",
            "Epoc: 13 BCE 0.99\n",
            "Epoc: 14 BCE 0.98\n",
            "Epoc: 15 BCE 0.97\n",
            "Epoc: 16 BCE 0.96\n",
            "Epoc: 17 BCE 0.95\n",
            "Epoc: 18 BCE 0.94\n",
            "Epoc: 19 BCE 0.93\n",
            "Epoc: 20 BCE 0.92\n",
            "Epoc: 21 BCE 0.91\n",
            "Epoc: 22 BCE 0.90\n",
            "Epoc: 23 BCE 0.89\n",
            "Epoc: 24 BCE 0.89\n",
            "Epoc: 25 BCE 0.88\n",
            "Epoc: 26 BCE 0.87\n",
            "Epoc: 27 BCE 0.87\n",
            "Epoc: 28 BCE 0.86\n",
            "Epoc: 29 BCE 0.86\n",
            "Epoc: 30 BCE 0.86\n",
            "Epoc: 31 BCE 0.85\n",
            "Epoc: 32 BCE 0.85\n",
            "Epoc: 33 BCE 0.84\n",
            "Epoc: 34 BCE 0.84\n",
            "Epoc: 35 BCE 0.84\n",
            "Epoc: 36 BCE 0.84\n",
            "Epoc: 37 BCE 0.83\n",
            "Epoc: 38 BCE 0.83\n",
            "Epoc: 39 BCE 0.83\n",
            "Epoc: 40 BCE 0.83\n",
            "Epoc: 41 BCE 0.83\n",
            "Epoc: 42 BCE 0.83\n",
            "Epoc: 43 BCE 0.82\n",
            "Epoc: 44 BCE 0.82\n",
            "Epoc: 45 BCE 0.82\n",
            "Epoc: 46 BCE 0.82\n",
            "Epoc: 47 BCE 0.82\n",
            "Epoc: 48 BCE 0.82\n",
            "Epoc: 49 BCE 0.82\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(range(len(cost)),cost)\n",
        "plt.ylabel('Binary Cross Entropy')\n",
        "plt.xlabel('Epoc')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Ysp4styjtBPJ",
        "outputId": "e44c1775-748c-424f-f8a0-b7891b625ec6"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "fpuUWyDOsAie"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "  train_pred=model(X_train)\n",
        "  #train_pred\n",
        "  train_acc=(torch.argmax(train_pred,dim=1)==y_train).float().mean()\n",
        "  print('Training Accuracy: %.2f' %train_acc)\n",
        "  test_pred=model(X_test)\n",
        "  test_acc=(torch.argmax(test_pred,dim=1)==y_test).float().mean()\n",
        "  print('Testing Accuracy: %.2f' %test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rZMw266gjQHU",
        "outputId": "89f6f4ff-1201-45b1-b883-c4dd123060c7"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.66\n",
            "Testing Accuracy: 0.70\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8G2OfLTfc5XW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Multinomial Logistic Regression**"
      ],
      "metadata": {
        "id": "AGcTfUjYnsSs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms"
      ],
      "metadata": {
        "id": "5Ig03Icalara"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.transforms import Transform\n",
        "transform=transforms.ToTensor()\n",
        "\n",
        "### Data Loading\n",
        "traindata=torchvision.datasets.MNIST(root='./data',train=True,download=True,transform=transform)\n",
        "trainloader=torch.utils.data.DataLoader(traindata,batch_size=32,shuffle=False)\n",
        "\n",
        "testdata=torchvision.datasets.MNIST(root='./data',train=False,download=True,transform=transform)\n",
        "testloader=torch.utils.data.DataLoader(testdata,batch_size=32,shuffle=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443,
          "referenced_widgets": [
            "9a359bbb51b247208a47a951dcb5b3b3",
            "99bb99c1a9474f7a88d6c325c568a738",
            "218e54983e9842b0bb128079e6f53186",
            "2ad49ead56d147afbc0dbc72f82ab5a2",
            "06ed7720d25a4eabbede0310561d73c1",
            "e80de037233a4246bbc1b9f845d1cf93",
            "afe1df4529914b97b939afe5cf2e2105",
            "77fa5bcc3ee641a99358f8aa950a65cc",
            "2835caeef5ba4e398e65f93a638824cf",
            "f1268730cd3f4fa98ce72895779f4cab",
            "e9f34a9c4b184df1b8945f7157d0993f",
            "0932c22eaf9e46048353beefb52561de",
            "30d02c4e14aa432d8975188adb9e5d7f",
            "45829e4a20354debad02e64304982248",
            "9e842ba3920e49fbaee0c4ffd686186c",
            "f1b467734a39458fb6360002d6b21cad",
            "28883ab0b7b1430ba990eb5901a3ad57",
            "28416d194af64a0fa1dc4241740ce229",
            "fa4f838ad6e845a3828631230a4f7cbc",
            "3748a5e0bd6c4a6882e8a193d79a94ea",
            "3de878b332ba455c8130930b8afc2492",
            "38859527a131488e94a50a24c2e5d08c",
            "c92b858d366341b0af36e765491e2acd",
            "d9d51ad4e0f6428caa287beecca13bf1",
            "754d877115b64b77976cff2b111b6356",
            "456b6eccbfe94c8da7a97bde08e0515c",
            "380cfe4a732d4a0298dff07701cfabe0",
            "2387752c392d432680a3f704015d256a",
            "96c7636da9d84f5bad70b806d6d8962f",
            "1bf9ad6bfc6b4c25aad23b7c307bf81c",
            "3881a5c1ea63474c837d0688575aecf8",
            "18641626327447599414803ff2b19904",
            "85cb9f1de4fe4a34a1ec3dd93e8886f1",
            "e5326f5315a844019b9b0534b563ae14",
            "0148443424cc4b7890bcac61ef1565b3",
            "926d35e4955a47c8a89a5b051a97d94b",
            "f625d2977ac6460b9dd5235d37a38673",
            "37602cb30ff34b4a983276a4a8c5894b",
            "0159596dcb7c4888a960f5e2c793fcc4",
            "7b6e9333f50c4e5db0211cb03ef4229d",
            "291c01fb43be40f89d60e964a641895e",
            "7d55788d77614b74abc7eea094f9a782",
            "86c21f4c9e5d4dec8e425b7249ef6694",
            "300a6f40040744ff87a65d6ce246fd35"
          ]
        },
        "id": "Hoxzhwj5n8Rd",
        "outputId": "93d2332e-a00b-4b2f-9c50-f164527310a1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9a359bbb51b247208a47a951dcb5b3b3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/28881 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0932c22eaf9e46048353beefb52561de"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c92b858d366341b0af36e765491e2acd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4542 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e5326f5315a844019b9b0534b563ae14"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "def imshow(img):\n",
        "  img=img.numpy()\n",
        "  img=np.transpose(img,(1,2,0))\n",
        "  plt.imshow(img)\n",
        "  plt.show()\n"
      ],
      "metadata": {
        "id": "j_KHD4c7rX3Q"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataiter=iter(trainloader)\n",
        "images,labels=next(dataiter)\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "print(labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "i3bxmbd6qysk",
        "outputId": "bdf6dc01-17f4-4aea-de0e-0a5772621849"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([5, 0, 4, 1, 9, 2, 1, 3, 1, 4, 3, 5, 3, 6, 1, 7, 2, 8, 6, 9, 4, 0, 9, 1,\n",
            "        1, 2, 4, 3, 2, 7, 3, 8])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "images.view(images.size(0),-1).shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KrsYYFyke55W",
        "outputId": "dc98ee53-f1ed-4c19-c7c2-28212f9c22c5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([32, 784])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(images[0].shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GP7q5F1gs_0U",
        "outputId": "7653d461-7867-4ee0-b1ca-e4eb44661aeb"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 28, 28])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "class LogisticNet(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.layer1=nn.Linear(1*28*28,100)\n",
        "    self.layer2=nn.Linear(100,30)\n",
        "    self.layer3=nn.Linear(30,10)\n",
        "    \n",
        "  def forward(self,x):\n",
        "    x=F.relu(self.layer1(x))\n",
        "    x=F.relu(self.layer2(x))\n",
        "    x=self.layer3(x)\n",
        "    return x\n",
        "\n",
        "\n",
        "\n",
        "model=LogisticNet()"
      ],
      "metadata": {
        "id": "W7thWGfVsSKj"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7BKjCNWXf2vM",
        "outputId": "d52695e6-e824-4440-bd26-6471dae9405a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticNet(\n",
              "  (layer1): Linear(in_features=784, out_features=100, bias=True)\n",
              "  (layer2): Linear(in_features=100, out_features=30, bias=True)\n",
              "  (layer3): Linear(in_features=30, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.optim as optim"
      ],
      "metadata": {
        "id": "3KqBCmozGYh7"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def train_mnst(model,train_loader,test_loader,EPOCHS=100,lr=.001,seed=32):\n",
        "  torch.manual_seed(seed)\n",
        "  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "  cost=[]\n",
        "  model.to(device)\n",
        "  optimizer=optim.SGD(model.parameters(),lr=lr)\n",
        "  loss_function=nn.CrossEntropyLoss()\n",
        "  for epoch in range(EPOCHS):\n",
        "\n",
        "    curr_loss=0.0\n",
        "    currect=0.0\n",
        "    t_currect=0.0\n",
        "    for i,data in enumerate(train_loader,0):\n",
        "\n",
        "\n",
        "      ## Get a Batch of Data\n",
        "      input,target=data[0].to(device),data[1].to(device)\n",
        "      ###1. Compute the output\n",
        "     \n",
        "      ###Flatten the input\n",
        "      input=input.view(input.size(0),-1)\n",
        "      yhat=model(input)\n",
        "      \n",
        "      ###2. Compute the loss\n",
        "      loss=loss_function(yhat,target)\n",
        "      \n",
        "\n",
        "      ###3. Compute the gradient\n",
        "\n",
        "      optimizer.zero_grad()\n",
        "      loss.backward()\n",
        "    \n",
        "      ###4. Update the parameters\n",
        "\n",
        "      optimizer.step()\n",
        "      ### Log the record\n",
        "      with torch.no_grad():\n",
        "        yhat=model(input)\n",
        "        err=loss_function(yhat,target)\n",
        "        curr_loss+=err\n",
        "        b_currect=((torch.argmax(yhat,dim=1)==target).sum()).float()\n",
        "      \n",
        "        currect+=b_currect\n",
        "    ### Calculate the accuracy    \n",
        "    with torch.no_grad():\n",
        "      for data in test_loader:\n",
        "          ims,lbs=data[0].to(device),data[1].to(device)\n",
        "          pred=model(ims.view(ims.size(0),-1))\n",
        "          t_currect+=(torch.argmax(pred,dim=1)==lbs).sum()\n",
        "   \n",
        "      print('Current Epoch: %3d' %(epoch+1),end=\"\")\n",
        "      print(' | Loss: %5.2f' %curr_loss,end=\"\")\n",
        "      accuracy=currect.sum()/len(traindata)*100\n",
        "      test_accuracy=t_currect.sum()/len(testdata)*100\n",
        "      #print(currect)\n",
        "      print(' | Accuracy: %.2f' %accuracy,end=\"\")\n",
        "      print(' | Test Accuracy: %.2f' %test_accuracy )\n",
        "  return cost\n",
        "      \n",
        "\n",
        "      \n"
      ],
      "metadata": {
        "id": "JRSpli1GHMRM"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cost=train_mnst(model,trainloader,testloader,EPOCHS=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sf_fgi1Xd-JQ",
        "outputId": "1d68165e-2714-4b88-db61-7f997302c0eb"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Current Epoch:   1 | Loss: 551.73 | Accuracy: 91.62 | Test Accuracy: 91.88\n",
            "Current Epoch:   2 | Loss: 544.33 | Accuracy: 91.70 | Test Accuracy: 91.95\n",
            "Current Epoch:   3 | Loss: 537.18 | Accuracy: 91.83 | Test Accuracy: 92.05\n",
            "Current Epoch:   4 | Loss: 530.23 | Accuracy: 91.92 | Test Accuracy: 92.15\n",
            "Current Epoch:   5 | Loss: 523.46 | Accuracy: 92.02 | Test Accuracy: 92.25\n",
            "Current Epoch:   6 | Loss: 516.81 | Accuracy: 92.13 | Test Accuracy: 92.31\n",
            "Current Epoch:   7 | Loss: 510.29 | Accuracy: 92.24 | Test Accuracy: 92.44\n",
            "Current Epoch:   8 | Loss: 503.90 | Accuracy: 92.36 | Test Accuracy: 92.53\n",
            "Current Epoch:   9 | Loss: 497.67 | Accuracy: 92.42 | Test Accuracy: 92.62\n",
            "Current Epoch:  10 | Loss: 491.54 | Accuracy: 92.52 | Test Accuracy: 92.68\n",
            "Current Epoch:  11 | Loss: 485.50 | Accuracy: 92.59 | Test Accuracy: 92.77\n",
            "Current Epoch:  12 | Loss: 479.56 | Accuracy: 92.69 | Test Accuracy: 92.85\n",
            "Current Epoch:  13 | Loss: 473.70 | Accuracy: 92.80 | Test Accuracy: 92.86\n",
            "Current Epoch:  14 | Loss: 467.95 | Accuracy: 92.90 | Test Accuracy: 92.93\n",
            "Current Epoch:  15 | Loss: 462.30 | Accuracy: 92.98 | Test Accuracy: 93.02\n",
            "Current Epoch:  16 | Loss: 456.77 | Accuracy: 93.06 | Test Accuracy: 93.10\n",
            "Current Epoch:  17 | Loss: 451.37 | Accuracy: 93.15 | Test Accuracy: 93.16\n",
            "Current Epoch:  18 | Loss: 446.07 | Accuracy: 93.24 | Test Accuracy: 93.23\n",
            "Current Epoch:  19 | Loss: 440.86 | Accuracy: 93.33 | Test Accuracy: 93.35\n",
            "Current Epoch:  20 | Loss: 435.75 | Accuracy: 93.38 | Test Accuracy: 93.39\n",
            "Current Epoch:  21 | Loss: 430.75 | Accuracy: 93.45 | Test Accuracy: 93.48\n",
            "Current Epoch:  22 | Loss: 425.84 | Accuracy: 93.54 | Test Accuracy: 93.52\n",
            "Current Epoch:  23 | Loss: 421.04 | Accuracy: 93.63 | Test Accuracy: 93.56\n",
            "Current Epoch:  24 | Loss: 416.33 | Accuracy: 93.69 | Test Accuracy: 93.58\n",
            "Current Epoch:  25 | Loss: 411.70 | Accuracy: 93.75 | Test Accuracy: 93.67\n",
            "Current Epoch:  26 | Loss: 407.15 | Accuracy: 93.81 | Test Accuracy: 93.73\n",
            "Current Epoch:  27 | Loss: 402.70 | Accuracy: 93.88 | Test Accuracy: 93.75\n",
            "Current Epoch:  28 | Loss: 398.31 | Accuracy: 93.93 | Test Accuracy: 93.77\n",
            "Current Epoch:  29 | Loss: 394.00 | Accuracy: 94.01 | Test Accuracy: 93.85\n",
            "Current Epoch:  30 | Loss: 389.79 | Accuracy: 94.08 | Test Accuracy: 93.92\n",
            "Current Epoch:  31 | Loss: 385.66 | Accuracy: 94.15 | Test Accuracy: 93.98\n",
            "Current Epoch:  32 | Loss: 381.61 | Accuracy: 94.21 | Test Accuracy: 94.04\n",
            "Current Epoch:  33 | Loss: 377.64 | Accuracy: 94.27 | Test Accuracy: 94.07\n",
            "Current Epoch:  34 | Loss: 373.73 | Accuracy: 94.32 | Test Accuracy: 94.13\n",
            "Current Epoch:  35 | Loss: 369.89 | Accuracy: 94.37 | Test Accuracy: 94.20\n",
            "Current Epoch:  36 | Loss: 366.13 | Accuracy: 94.42 | Test Accuracy: 94.21\n",
            "Current Epoch:  37 | Loss: 362.42 | Accuracy: 94.50 | Test Accuracy: 94.24\n",
            "Current Epoch:  38 | Loss: 358.79 | Accuracy: 94.56 | Test Accuracy: 94.32\n",
            "Current Epoch:  39 | Loss: 355.22 | Accuracy: 94.61 | Test Accuracy: 94.38\n",
            "Current Epoch:  40 | Loss: 351.73 | Accuracy: 94.67 | Test Accuracy: 94.41\n",
            "Current Epoch:  41 | Loss: 348.29 | Accuracy: 94.73 | Test Accuracy: 94.48\n",
            "Current Epoch:  42 | Loss: 344.91 | Accuracy: 94.77 | Test Accuracy: 94.51\n",
            "Current Epoch:  43 | Loss: 341.58 | Accuracy: 94.82 | Test Accuracy: 94.52\n",
            "Current Epoch:  44 | Loss: 338.31 | Accuracy: 94.89 | Test Accuracy: 94.57\n",
            "Current Epoch:  45 | Loss: 335.10 | Accuracy: 94.93 | Test Accuracy: 94.60\n",
            "Current Epoch:  46 | Loss: 331.94 | Accuracy: 94.98 | Test Accuracy: 94.68\n",
            "Current Epoch:  47 | Loss: 328.82 | Accuracy: 95.01 | Test Accuracy: 94.73\n",
            "Current Epoch:  48 | Loss: 325.75 | Accuracy: 95.05 | Test Accuracy: 94.75\n",
            "Current Epoch:  49 | Loss: 322.74 | Accuracy: 95.09 | Test Accuracy: 94.76\n",
            "Current Epoch:  50 | Loss: 319.76 | Accuracy: 95.13 | Test Accuracy: 94.83\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(traindata)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bdZxUSxj4p_G",
        "outputId": "1f365309-fc83-435d-aee7-ec6d19ed8695"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "prob=model(images.view(32,-1).to(device))\n",
        "(torch.argmax(prob,dim=1).to('cpu')==labels).sum().float()/32*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U170Hp0Y4a0L",
        "outputId": "d0e7dcb9-cf48-4589-be2d-cc2e12be0fc3"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(96.8750)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    }
  ]
}